





from __future__ import absolute_import, division, print_function, unicode_literals

from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
    ROBERTA_INPUTS_DOCSTRING,
    ROBERTA_START_DOCSTRING,
    RobertaEmbeddings,
)

from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy

@add_start_docstrings(
    '''The RoBERTa Model transformer with early exiting (DeeRoBERTa). '''		,   __lowercase		,   )
class   A							(						__lowercase    ):
      _snake_case				  =RobertaConfig
      _snake_case				  ='''roberta'''



      def __init__(						self:    Any		,	_lowerCAmelCase:    Tuple							)    ->     Any:
         '''simple docstring'''

         super().__init__(_lowerCAmelCase							)

         UpperCAmelCase_           =RobertaEmbeddings(_lowerCAmelCase							)
         self.init_weights()

@add_start_docstrings(
    '''RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,
    also takes care of multi-layer training. '''		,   __lowercase		,   )
class   A							(						__lowercase    ):
      _snake_case				  =RobertaConfig
      _snake_case				  ='''roberta'''



      def __init__(						self:    int		,	_lowerCAmelCase:    List[str]							)    ->     Any:
         '''simple docstring'''

         super().__init__(_lowerCAmelCase							)
         UpperCAmelCase_           =config.num_labels
         UpperCAmelCase_           =config.num_hidden_layers

         UpperCAmelCase_           =DeeRobertaModel(_lowerCAmelCase							)
         UpperCAmelCase_           =nn.Dropout(config.hidden_dropout_prob							)
         UpperCAmelCase_           =nn.Linear(config.hidden_size		,	self.config.num_labels							)






      @add_start_docstrings_to_model_forward(_lowerCAmelCase							)
      def 						lowerCAmelCase__     (						self:    Any		,	_lowerCAmelCase:    Any=None		,	_lowerCAmelCase:    int=None		,	_lowerCAmelCase:    Optional[Any]=None		,	_lowerCAmelCase:    Dict=None		,	_lowerCAmelCase:    str=None		,	_lowerCAmelCase:    List[Any]=None		,	_lowerCAmelCase:    Optional[int]=None		,	_lowerCAmelCase:    List[str]=-1		,	_lowerCAmelCase:    Dict=False		,	)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           =self.num_layers
         try:
            UpperCAmelCase_           =self.roberta(
                _lowerCAmelCase		,	attention_mask=_lowerCAmelCase		,	token_type_ids=_lowerCAmelCase		,	position_ids=_lowerCAmelCase		,	head_mask=_lowerCAmelCase		,	inputs_embeds=_lowerCAmelCase		,	)

            UpperCAmelCase_           =outputs[1]

            UpperCAmelCase_           =self.dropout(_lowerCAmelCase							)
            UpperCAmelCase_           =self.classifier(_lowerCAmelCase							)
            UpperCAmelCase_           =(logits,) + outputs[2:]  # add hidden states and attention if they are here
         except HighwayException as e:
            UpperCAmelCase_           =e.message
            UpperCAmelCase_           =e.exit_layer
            UpperCAmelCase_           =outputs[0]

         if not self.training:
            UpperCAmelCase_           =entropy(_lowerCAmelCase							)
            UpperCAmelCase_           =[]
            UpperCAmelCase_           =[]
         if labels is not None:
            if self.num_labels == 1:
               #  We are doing regression
               UpperCAmelCase_           =MSELoss()
               UpperCAmelCase_           =loss_fct(logits.view(-1							)		,	labels.view(-1							)							)
            else:
               UpperCAmelCase_           =CrossEntropyLoss()
               UpperCAmelCase_           =loss_fct(logits.view(-1		,	self.num_labels							)		,	labels.view(-1							)							)

            # work with highway exits
            UpperCAmelCase_           =[]
            for highway_exit in outputs[-1]:
               UpperCAmelCase_           =highway_exit[0]
               if not self.training:
                  highway_logits_all.append(_lowerCAmelCase							)
                  highway_entropy.append(highway_exit[2]							)
               if self.num_labels == 1:
                  #  We are doing regression
                  UpperCAmelCase_           =MSELoss()
                  UpperCAmelCase_           =loss_fct(highway_logits.view(-1							)		,	labels.view(-1							)							)
               else:
                  UpperCAmelCase_           =CrossEntropyLoss()
                  UpperCAmelCase_           =loss_fct(highway_logits.view(-1		,	self.num_labels							)		,	labels.view(-1							)							)
               highway_losses.append(_lowerCAmelCase							)

            if train_highway:
               UpperCAmelCase_           =(sum(highway_losses[:-1]							),) + outputs
               # exclude the final highway, of course
            else:
               UpperCAmelCase_           =(loss,) + outputs
         if not self.training:
            UpperCAmelCase_           =outputs + ((original_entropy, highway_entropy), exit_layer)
            if output_layer >= 0:
               UpperCAmelCase_           =(
                   (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
               )  # use the highway of the last layer

         return outputs  # (loss), logits, (hidden_states), (attentions), entropy











"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						lowercase   (   unittest.TestCase   ):





      @property
      def        _snake_case     (			self					)						->	List[str]:
            torch.manual_seed(0					)
            lowerCAmelCase    		=			UNetaDModel(
                block_out_channels=(32, 64) ,		layers_per_block=2 ,		sample_size=32 ,		in_channels=3 ,		out_channels=3 ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,		)
            return model





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			self.dummy_uncond_unet
            lowerCAmelCase    		=			ScoreSdeVeScheduler()

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase ,		return_dict=lowercase					)[
                0
            ]

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]
            lowerCAmelCase    		=			image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase    		=			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2







@slow
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			"""google/ncsnpp-church-256"""
            lowerCAmelCase    		=			UNetaDModel.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVeScheduler.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=10 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)

            lowerCAmelCase    		=			np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






